Research on Spectral Data Feature Extraction Based on Wavelet Decomposition

被引:1
作者
Chen Gang [1 ,3 ]
Chen Xiao-mei [1 ,2 ]
Li Ting [1 ,2 ]
Ni Guo-qiang [1 ,2 ]
机构
[1] Beijing Inst Technol, Sch Optoelect, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Minist Educ China, Key Lab Photoelect Imaging Technol & Syst, Beijing 100081, Peoples R China
[3] NYU, Polytech Inst, Brooklyn, NY 11201 USA
关键词
Spectral analysis; Feature extraction; Wavelet decomposition;
D O I
10.3964/j.issn.1000-0593(2010)11-3027-04
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Reflectance spectral curve reveals the unique physical characteristic of different materials Through spectral match and recognition different materials could be distinguished Because of the great amount of spectral data and the ambiguous absorption feature of original spectral curve feature extraction of reflectance spectral curve is one of the essential techniques in hyperspectral image classification and recognition. Using wavelet decomposition technique the present paper proposes a new spectral feature extraction algorithm to compress data amount while reserve spectral feature selectively Through selecting the appropriate decomposition level by measuring the objective absorption feature frequency the original signal would be projected into a new feature space with less data amount and more obvious objective feature than the original one The experiments show that the method proposed can reduce the spectrum dimensions effectively and Improve the recognition precision with the spectrum matching
引用
收藏
页码:3027 / 3030
页数:4
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